Turner Construction Data Analyst Interview Questions and Answers

Q1. What are joins and its types?
Ans. 
Joins are used to combine rows from two or more tables based on a related column between them.
• Types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

• INNER JOIN returns rows when there is at least one match in both tables.

• LEFT JOIN returns all rows from the left table and the matched rows from the right table.

• RIGHT JOIN returns all rows from the right table and the matched rows from the left table.

• FULL ...read more
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Q2. What are aggregate functions?
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Aggregate functions are functions in databases that perform a calculation on a set of values and return a single value.
• Aggregate functions are used to perform operations on a group of rows and return a single result.

• Common aggregate functions include SUM, AVG, COUNT, MIN, and MAX.

• For example, SUM function calculates the total of a column, AVG calculates the average, COUNT counts the number of rows, MIN finds the minimu
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Q1. Coding questions in python eda part
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Q1. Where do u see in next 5 years
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30 questions and time was approx. 45 min to 60 min

There were some basic as well as some high level questions

Basic aptitude questions

Coding questions hard level like matrix etc

Q1. What is Data sharding
Ans. 
Data sharding is the process of splitting a database into smaller, more manageable parts called shards.
• Data sharding helps distribute data across multiple servers to improve performance and scalability.

• Each shard contains a subset of the data, allowing for parallel processing and faster query times.

• Common sharding techniques include range-based sharding, hash-based sharding, and key-based sharding.

• Examples of data shar...read more
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Q2. What is clustering
Ans. 
Clustering is a technique used in data analysis to group similar data points together based on certain criteria.
• Clustering is an unsupervised machine learning technique.

• It is used to discover hidden patterns or groupings in data.

• Common clustering algorithms include K-means, hierarchical clustering, and DBSCAN.

• Example: clustering customers based on their purchasing behavior to identify different market segments.
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Q1. Based on your profile
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Q2. Knowledge on Data analyst softwares
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Q1. Python question for data cleaning
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Q2. Sql question on data manipulation
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Python programming for 45 for minutes

Q1. What are the hyperparameters of SVM
Ans. 
Hyperparameters of SVM include C, kernel, gamma, degree, and coef0.
• C: Regularization parameter that controls the trade-off between achieving a low error on the training data and minimizing model complexity.

• Kernel: Specifies the type of hyperplane used to separate the data.

• Gamma: Kernel coefficient for 'rbf', 'poly', and 'sigmoid' kernels.

• Degree: Degree of the polynomial kernel function.

• Coef0: Independent term in kernel...read more
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Q2. How to tune that hyperparameters
Ans. 
Hyperparameters can be tuned using techniques like grid search, random search, and Bayesian optimization.
• Grid search: Exhaustively search through a specified subset of hyperparameters.

• Random search: Randomly sample hyperparameter combinations.

• Bayesian optimization: Use probabilistic models to predict the performance of different hyperparameter configurations.
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